Data item aggregate probability analysis system

ABSTRACT

Computer-implemented systems and methods are disclosed for automatically aggregating, analyzing, and presenting probabilities associated with data items. Data items may be associated with probabilities or risks, and the data items may have various characteristics. A grouping of data items may be determined based on these characteristics, and probabilities within groups of data items may be aggregated and analyzed. Aggregated probabilities may be used to determine incremental probabilities for individual data items, to assess cumulative risk associated with a group of data items, and to analyze probabilities associated with a particular data item group. User interfaces may be generated to facilitate selection and grouping of data items, selection of risk models, and analysis of aggregate probabilities.

TECHNICAL FIELD

The present disclosure relates to systems and techniques for dataintegration, analysis, and visualization. More specifically, the presentdisclosure relates to systems and techniques for analyzing data itemprobabilities that can be grouped and aggregated according to variouscriteria.

BACKGROUND

Electronic record-keeping produces data sets with thousands or millionsof records. The sheer quantity of information available for analysis mayprevent meaningful conclusions from being drawn, or may prevent analysisfrom even being attempted. Data items in data sets may be associatedwith probabilities or risks that can be aggregated across various itemdimensions, and an analysis of the probabilities associated with aparticular data item may be incomplete or misleading without furtherconsideration and analysis of aggregate probabilities.

SUMMARY

The systems, methods, and devices described herein each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this disclosure, severalnon-limiting features will now be discussed briefly.

Embodiments of the present disclosure relate to the visualization andanalysis of aggregate probabilities associated with groups of dataitems. Data items may be grouped according to various criteria, and theprobabilities associated with a group of data items may be aggregatedand analyzed. Data item probabilities may be determined according tovarious risk models, and filtering criteria may be applied to selectdata items of interest. Interactive user interfaces may be generated forthe display, analysis, and filtering of data items, and the risk models,display modes, and filters may be modified via the user interface. Theseembodiments of the present disclosure allow data item probabilities tobe assessed and analyzed more efficiently, and facilitate detection ofsystemic and aggregate risks that are not otherwise detectable.

Accordingly, in various embodiments, large amounts of data areautomatically and dynamically calculated interactively in response touser inputs, and the calculated data is efficiently and compactlypresented to a user by the system. Thus, in some embodiments, the userinterfaces described herein are more efficient as compared to previoususer interfaces in which data is not dynamically updated and compactlyand efficiently presented to the user in response to interactive inputs.

Further, as described herein, the system may be configured and/ordesigned to generate user interface data useable for rendering thevarious interactive user interfaces described. The user interface datamay be used by the system, and/or another computer system, device,and/or software program (for example, a browser program), to render theinteractive user interfaces. The interactive user interfaces may bedisplayed on, for example, electronic displays (including, for example,touch-enabled displays).

Additionally, it has been noted that design of computer user interfaces“that are useable and easily learned by humans is a non-trivial problemfor software developers.” (Dillon, A. (2003) User Interface Design.MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan,453-458.) The various embodiments of interactive and dynamic userinterfaces of the present disclosure are the result of significantresearch, development, improvement, iteration, and testing. Thisnon-trivial development has resulted in the user interfaces describedherein which may provide significant cognitive and ergonomicefficiencies and advantages over previous systems. The interactive anddynamic user interfaces include improved human-computer interactionsthat may provide reduced mental workloads, improved decision-making,reduced work stress, and/or the like, for a user. For example, userinteraction with the interactive user interfaces described herein mayprovide an optimized display of geographic information and may enable auser to more quickly access, navigate, assess, and digest suchinformation than previous systems.

Further, the interactive and dynamic user interfaces described hereinare enabled by innovations in efficient interactions between the userinterfaces and underlying systems and components. For example, disclosedherein are improved methods of receiving user inputs, translation anddelivery of those inputs to various system components, automatic anddynamic execution of complex processes in response to the inputdelivery, automatic interaction among various components and processesof the system, and automatic and dynamic updating of the userinterfaces. The interactions and presentation of data via theinteractive user interfaces described herein may accordingly providecognitive and ergonomic efficiencies and advantages over previoussystems.

Various embodiments of the present disclosure provide improvements tovarious technologies and technological fields. For example, as describedabove, existing technologies for analyzing aggregate probabilities arelimited in various ways (e.g., they are slow and cumbersome, theyrequire more resources than can practically be made available, etc.),and various embodiments of the disclosure provide significantimprovements over such technology. Additionally, various embodiments ofthe present disclosure are inextricably tied to computer technology. Inparticular, various embodiments rely on detection of user inputs viagraphical user interfaces, calculation of updates to displayedelectronic data based on those user inputs, automatic processing andupdating of related item categories and associated risks, andpresentation of the updates via interactive graphical user interfaces.Such features and others (e.g., generation of aggregate risks associatedwith various item categories) are intimately tied to, and enabled by,computer technology, and would not exist except for computer technology.For example, the interactions with displayed data described below inreference to various embodiments cannot reasonably be performed byhumans alone, without the computer technology upon which they areimplemented. Further, the implementation of the various embodiments ofthe present disclosure via computer technology enables many of theadvantages described herein, including more efficient interaction with,and presentation of, various types of electronic image data.

Additional embodiments of the disclosure are described below inreference to the appended claims, which may serve as an additionalsummary of the disclosure.

In various embodiments, systems and/or computer systems are disclosedthat comprise a computer readable storage medium having programinstructions embodied therewith, and one or more processors configuredto execute the program instructions to cause the one or more processorsto perform operations comprising one or more aspects of the above-and/or below-described embodiments (including one or more aspects of theappended claims).

In various embodiments, computer-implemented methods are disclosed inwhich, by one or more processors executing program instructions, one ormore aspects of the above- and/or below-described embodiments (includingone or more aspects of the appended claims) are implemented and/orperformed.

In various embodiments, computer program products comprising a computerreadable storage medium are disclosed, wherein the computer readablestorage medium has program instructions embodied therewith, the programinstructions executable by one or more processors to cause the one ormore processors to perform operations comprising one or more aspects ofthe above- and/or below-described embodiments (including one or moreaspects of the appended claims).

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, whichillustrate exemplary embodiments of the present disclosure. In thedrawings:

FIG. 1 is a schematic block diagram of an example network topologyincluding a categorized risk modeling server, a categorized riskpresentation server, an item data store, and a risk model data store incommunication with a client computing device via a network;

FIGS. 2A-2D are block diagrams of example user interfaces for presentingand interacting with aggregate probability analyses in accordance withaspects of the present disclosure;

FIG. 3 is an example block diagram depicting aggregation, analysis, andpresentation of categorized risks in accordance with aspects of thepresent disclosure;

FIG. 4 is a flow diagram depicting an example routine for analyzingprobabilities associated with categorized data items in accordance withaspects of the present disclosure;

FIG. 5 is a flow diagram depicting an example routine for analyzingprobabilities associated with item categories in accordance with aspectsof the present disclosure; and

FIG. 6 is a block diagram of an example computer system consistent withembodiments of the present disclosure.

DETAILED DESCRIPTION

Overview

Reference will now be made in detail to example embodiments, theexamples of which are illustrated in the accompanying drawings. Wheneverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

Embodiments of the present disclosure relate to systems, methods, andcomputer-readable mediums for automatically analyzing, and displayingaggregate risks or probabilities associated with data items that can begrouped according to their characteristics. Data items may be grouped,for example, according to geographic locations associated with the dataitems, business categories associated with the data items, or othercriteria. Probabilities associated with the data items may be aggregatedto determine and analyze probabilities for geographic regions or itemcategories. Data item probabilities may be determined or obtainedaccording to various risk models, and data items and probabilities maybe filtered and displayed to facilitate analysis.

For example, data items may represent real estate properties, and mayhave attributes that include geographic locations and property values.These data items may be grouped and analyzed using a risk model todetermine a probability of, for example, loss due to flood, wildfire, orother event. As a further example, data items may represent shippinglanes, transit corridors, or individual freight vehicles, and may haveattributes that include a geographic area of travel, mode oftransportation, rate of travel, and the like. These data items may begrouped and analyzed using a risk model to determine a probability ofdelayed transit due to inclement weather, traffic congestion, a laborstrike at a shipping terminal, or other factors. As a still furtherexample, data items may represent small businesses, and may haveattributes that allow categorization of the businesses into retailsales, household services, and the like. These data items may be groupedand analyzed using a risk model to determine, for example, economic riskassociated with a change in the business climate. More generally,aspects of the present disclosure include any set of data items that canbe associated with probabilities.

Terms

In order to facilitate an understanding of the systems and methodsdiscussed herein, a number of terms are defined below. The terms definedbelow, as well as other terms used herein, should be construed toinclude the provided definitions, the ordinary and customary meaning ofthe terms, and/or any other implied meaning for the respective terms.Thus, the definitions below do not limit the meaning of these terms, butonly provide exemplary definitions.

Choropleth Map: A type of geographic map display in which predefinedgeographic regions are colored or shaded according to a variable ofinterest, such as a cumulative risk or probability for data itemsassociated with locations within the geographic region.

Data Store: Any computer readable storage medium and/or device (orcollection of data storage mediums and/or devices). Examples of datastores include, but are not limited to, optical disks (e.g., CD-ROM,DVD-ROM, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.),memory circuits (e.g., solid state drives, random-access memory (RAM),etc.), and/or the like. Another example of a data store is a hostedstorage environment that includes a collection of physical data storagedevices that may be remotely accessible and may be rapidly provisionedas needed (commonly referred to as “cloud” storage).

Database: Any data structure (and/or combinations of multiple datastructures) for storing and/or organizing data, including, but notlimited to, relational databases (e.g., Oracle databases, MySQLdatabases, etc.), non-relational databases (e.g., NoSQL databases,etc.), in-memory databases, spreadsheets, as comma separated values(CSV) files, eXtendible markup language (XML) files, TeXT (TXT) files,flat files, spreadsheet files, and/or any other widely used orproprietary format for data storage. Databases are typically stored inone or more data stores. Accordingly, each database referred to herein(e.g., in the description herein and/or the figures of the presentapplication) is to be understood as being stored in one or more datastores.

Data Item or Item: A data container for information representingspecific things in the world that have a number of definable properties.For example, a data item can represent an entity such as a physicalobject, a parcel of land or other real property, a market instrument, apolicy or contract, or other noun. Each data item may be associated witha unique identifier that uniquely identifies the data item. The item'sattributes (e.g. metadata about the object) may be represented in one ormore properties. Attributes may include, for example, a geographiclocation associated with the item, a value associated with the item, aprobability associated with the item, an event associated with the item,and so forth.

Event: A change to a data item attribute, which may correspond to achange in the real-world entity represented by the data item. An eventmay be, for example, an increase or decrease in the value associatedwith the item.

Geographic Data Item or Geographic Item: A data item having an attributethat associates the data item with a particular geographic location,area, or region.

Heat Map: A type of geographic map display in which the regions to becolored or shaded are determined according to a variable of interest. Acommon example of a heat map is a weather forecast, in which the regionthat is shaded to indicate high temperatures in the 70-79° range varieson a day-to-day basis.

Probability or Risk: The likelihood that an event associated with aparticular data item will occur.

Probability Category: A collection of data items that have similarprobabilities and/or other attributes. For example, a probabilitycategory may include data items having a high probability, or data itemshaving a low probability and a high value.

Properties: Attributes of a data item that represent individual dataitems. At a minimum, each property of a data item has a property typeand a value or values.

Property Type: The type of data a property is, such as a string, aninteger, or a double. Property types may include complex property types,such as a series data values associated with timed ticks (e.g. a timeseries), etc.

Property Value: The value associated with a property, which is of thetype indicated in the property type associated with the property. Aproperty may have multiple values.

Risk Premium: A portion of a data item attribute that is attributed tothe risk(s) associated with the data item. A risk premium may beexpressed as a ratio, percentage, or amount. For example, an insurancepolicy on a parcel of land in a flood plain may have a higher cost thana policy on an equivalent parcel of land that is not in a flood plain.The risk premium may be expressed as the ratio of, or the delta between,these two values.

Transit Corridor: A path that may be followed when moving people orobjects from one geographic point to another. A transit corridor may beassociated with a data item, or may itself be a data item. Examples oftransit corridors include highways and roadways, shipping lanes, flightpaths, rivers, bus routes, subway lines, and so forth.

EXAMPLE DEVICES/SYSTEMS

FIG. 1 is a block diagram of an example network topology 100 fordetermining, aggregating, and analyzing data item probabilities inaccordance with the present disclosure. In the illustrated embodiment,the network topology 100 includes an item data store 102, a risk modeldata store 104, a categorized risk modeling server 106, and acategorized risk presentation server 108 in communication with a clientcomputing device 130 via a network 120. Generally, an item data store102 may correspond to a hard disk drive, network accessible storage, orany other type of perpetual or substantially perpetual storageaccessible by the geographic risk modeling server 106. For example, theitem data store 102 of FIG. 1 may correspond to network accessiblestorage devices. Though depicted as external to the categorized riskmodeling server 106, in some embodiments, the item data store 102 may beinternal to the categorized risk modeling server 106.

In some embodiments, the network topology 100 further includes a riskmodel data store 104 in communication with the categorized risk modelingserver 106. Generally, a risk model data store 104 may correspond to ahard disk drive, network accessible storage, or any other type ofperpetual or substantially perpetual storage accessible by thegeographic risk modeling server 106. For example, the risk model datastore 104 of FIG. 1 may correspond to a network accessible storagedevice. Though depicted as external to the categorized risk modelingserver 106, in some embodiments, the risk model data store 104 may beinternal to the categorized risk modeling server 106. Further, in someembodiments, the risk model data store 104 may be omitted or combinedwith the item data store 102. Still further, in some embodiments, therisk model data store 104 and/or the item data store 102 may beimplemented as a data pipeline system. Data pipeline systems aredescribed in more detail in U.S. patent application Ser. No. 15/287,715,titled “Universal Data Pipeline,” and filed Oct. 6, 2016; U.S. patentapplication Ser. No. 15/262,207, titled “Data Revision Control inLarge-Scale Data Analytic Systems,” and filed Sep. 12, 2016; and U.S.Provisional Patent Application No. 62/349,548, titled “Recording andTransforming Data in a Data Pipeline System,” and filed Jun. 13, 2016.The entire disclosure of each of the above items is hereby made part ofthis specification as if set forth fully herein and incorporated byreference for all purposes, for all that it contains.

In various embodiments, the categorized risk modeling server 106 maycorrespond to a wide variety of computing devices configured toimplement aspects of the present disclosure. For example, thecategorized risk modeling server 106 can include one or more computingdevices (e.g., server(s)), memory storing data and/or softwareinstructions (e.g., database(s), memory device(s), etc.), and otherknown computing components. According to some embodiments, thecategorized risk modeling server 106 can include one or more networkedcomputers that execute processing in parallel or use a distributedcomputing architecture. The categorized risk modeling server 106 can beconfigured to communicate with one or more components of the networktopology 100, and it can be configured to provide information via aninterface(s) accessible by users over a network (e.g., the Internet). Insome embodiments, the categorized risk modeling server 106 can includean application server configured to provide data to one or morecategorized risk presentation servers 108 executing on computing systemsconnected to the categorized risk modeling server 106.

The network topology 100 further includes a network 120 operable toenable communication between the categorized risk presentation server108 and the client computing device 130. The network 120 may be, forinstance, a wide area network (WAN), a local area network (LAN), or aglobal communications network. In some embodiments, the item data store102, a risk model data store 104, a categorized risk modeling server106, and a categorized risk presentation server 108 may communicate viathe network 120 or via a separate network, such as a private LAN.

The modules or components illustrated in FIG. 1 may include additionalcomponents, systems, and subsystems for facilitating the methods andprocesses. For example, in various embodiments, the item data store 102,risk model data store 104, categorized risk modeling server 106, andcategorized risk presentation server 108 may be centralized in onecomputing device, distributed across several computing devices,implemented by one or more virtual machine instances, and/or distributedthrough a network.

With reference now to FIGS. 2A-2D, example user interfaces 200, 220,240, and 260 for presentation of aggregated data item probabilityanalyses will be described. FIG. 2A depicts an example user interface200 in which data items, such as data items 212A-C, are displayed inrelation to coordinates on a geographic map 210. The user interface 200may be generated, for example, by the categorized risk presentationserver 108 of FIG. 1. The user interface 200 may include a legend 202,which relates data items to a set of categories. In some embodiments,the categories may be various types of data items. For example, thecategories may be commercial properties, residential properties,undeveloped land, and so forth. As further examples, the categories maybe related to a size of a business (e.g., number of employees, revenue,etc.), a type of insurance policy (home, auto, property, etc.), or othertypes of data items. In further embodiments, the categories may relateto probabilities associated with the data items. For example, thecategories may be a high-risk category, a medium-risk category, and alow-risk category. As further examples, the categories may be based onrisk-to-loss ratios, risk premiums, or other metrics associated withdata item probabilities.

The user interface 200 may further display a risk model selector 204,which enables a user to select various risk models. Various risk modelsmay be applied to the data items to determine and associate a number ofprobabilities for each data item. For example, a risk model maydetermine a probability for a data item that corresponds to a risk ofearthquake damage to a building. The probability may be determined basedon data item attributes such as structural materials, building codes,and so forth. The risk model may also incorporate other factors such asearthquake fault lines, the footprint of previous earthquakes at or nearthe geographic location of the data item, the amount of damage caused byprevious earthquakes in the region, and so on. As another example, otherrisk models may determine different probabilities for the same set ofdata items, such as a probability of flood damage or of an increase inproperty value. In some embodiments, the risk model may determine anexpected value for the data item based on the probability of an eventand the change in value that results from the event occurring. Forexample, the risk model may determine a 5% chance of damage that resultsin total economic loss of a building valued at $10 million. The riskmodel may therefore determine that the risk has an expected value of$500,000. In further embodiments, the risk model may determine andaggregate multiple expected values relating to various probabilities andcorresponding increases or decreases in an item attribute.

The user interface 200 may further display a risk region checkbox 206,which enables the user to select whether a risk region 214 will bedisplayed on the geographic map 210. The risk region 214 may correspondto a particular geographic region of interest. As examples, the riskregion 214 may be a low-lying area that is vulnerable to flooding, adistrict that has been zoned for industrial use or a particular taxrate, the actual or projected path of a storm, an area with particulardemographics or population densities, or other characteristics ofinterest. In some embodiments, the user interface 200 may includemultiple risk region checkboxes 206 or other controls that enable theuser to select a number of regions of interest to overlay on thegeographic map 210. In some embodiments, a set of risk regions 214 maycorrespond to a single external phenomenon. For example, a set of riskregions 214 may represent the path of a hurricane, and may includeregions corresponding to the storm's intensity (e.g., Category 1,Category 2, etc.), relative distance from the storm's center, and so on.In further embodiments, the set of risk regions 214 may include regionsbased on geographic characteristics such as population density or zoningregulations, or regions based on data item attributes such as averageproperty values or construction materials.

The user interface 200 may further display statistics regarding aselected risk region 214, such as the number of data items within therisk region 214, the aggregate values of data items within the riskregion 214, aggregate probabilities for data items within the riskregion 214, or other information. The visual display of risk regions 214and corresponding statistics in the user interface 200 providescognitive efficiencies and enables a user to analyze probabilitiesassociated with particular regions more quickly.

The geographic map 210 may display a number of data items, such as dataitems 212A-C, at geographic locations or areas associated with the dataitems. In the illustrated embodiment, the data items 212A-C may bedisplayed using various sizes, shapes, shadings, and colors to indicate,for example, categories of data items, risks or other related metricsassociated with the data items, attributes of the data items, and othercharacteristics. The geographic map 210 may further display an area ofinterest, as discussed above. The geographic map 210 may still furtherdisplay one or more transit corridors 216, which may be displayed usingvarious sizes, shapes, shadings, and colors in similar or correspondingfashion to the data items 212A-C. Transit corridors 216 may include, forexample, sea lanes, bus routes, rail or subway routes, air corridors,planned or actual driving routes for commercial trucks and vehicles, andthe like. In some embodiments, transit corridors may overlap one or morerisk regions 214, and the geographic map 210 may display the overlappingregions, risks associated with the transit corridor 216 and theoverlapping region, aggregate risks associated with the transitcorridor, and other information. In further embodiments, the userinterface 200 may display risks associated with delays, loss of cargo,loss in economic value, or combinations thereof. For example, thegeographic map 210 may display a sea lane that overlaps the path of astorm, and may display a risk that the cargo will be lost at sea or arisk that the shipment will be delayed. In still further embodiments,the user interface 200 may display a risk of loss associated withshipment delays, such as a missed window of opportunity of sales (e.g.,a holiday shopping season), expiration of perishable goods, penaltiesincurred for missing a contractual deadline, or other losses associatedwith delay. By displaying aggregated risks for an entire transitcorridor 216, the geographic map 210 and the user interface 200 allowfor more complete and efficient assessment of risks.

The user interface 200 may include more or fewer elements than thoseincluded for purposes of example in FIG. 2A. For example, the userinterface 200 may include controls that enable the user to select ordefine the categories, filter the data items according to selectedcriteria, zoom or pan the geographic area displayed in the map 210,change the level of detail displayed on the map 210, or other controls.As a further example, the geographic map 210 may allow user selection ofa particular data item, such as data item 212A, and may displayadditional information regarding the selected data item 212A. In someembodiments, the user interface 200 may include controls enabling theuser to create, edit, delete, import, and/or export data items. Stillfurther, the geographic map 210 may allow the user to define an area ofinterest, and may display statistics or other information regarding dataitems within the user-defined area of interest.

Turning now to FIG. 2B, an example user interface 220 for displayingprobability analyses relating to geographic regions will be described.The user interface 220 may be generated, for example, by the geographicrisk presentation server 108 of FIG. 1. The user interface 220 mayinclude a legend 202, risk model selector 204, and risk region checkbox206, which perform similar functions to those described with referenceto FIG. 2A. The user interface 220 may further include a map display230, which displays a choropleth map 232. The choropleth map 232 maydisplay risk categories associated with predefined geographic regions.The risk categories may be determined based on data items within eachregion, whose individual risk or probability attributes may beaggregated to determine a risk category for the region.

In some embodiments, the map display 230 may display a heat map, inwhich the boundaries of the geographic regions to be shaded aredetermined based on the data items, rather than the choropleth mapillustrated in FIG. 2B. In further embodiments, the map display 230 maydisplay a heat map as an overlay. In still further embodiments, the mapdisplay 230 may enable user selection of a particular region, and maydisplay information including the aggregated or cumulative riskassociated with the region, the data items within the region, and/orother information regarding the region.

Turning now to FIG. 2C, an example user interface 240 for displayingprobability analyses for data items relative to each other will bedescribed. The user interface 240 may be generated, for example, by thecategorized risk presentation server 108 of FIG. 1. The user interface240 may include a legend 202 and risk model selector 204, which performsimilar functions to those described with reference to FIG. 2A. In someembodiments, the user interface 240 may further include controls thatenable the user to filter the displayed data items to include only thosewithin an area of interest, or to apply other filtering criteria.

The user interface 240 may further include a scatterplot display 250,which may display and categorize data items (such as data item 252). Insome embodiments, the user interface 240 may include controls thatenable the user to specify the axes to use for the scatterplot display,the data items and/or categories of data items to display, and so forth.In other embodiments, the data items and/or categories may be selectedthrough another user interface, such as the user interface 220 describedabove with reference to FIG. 2B. For example, the map display 230 ofFIG. 2B may have selectable geographic regions, and selection of ageographic region may pre-populate the user interface 240 with dataitems corresponding to that geographic region. In further embodiments,the user interface 240 may enable the user to specify more than twoaxes, and may display one or more two- or three-dimensional scatterplotsusing the specified axes. Illustratively, the axes may include arisk/loss ratio, risk premium, a maximum total loss, or other attributesof the data item or characteristics of the associated risk(s).

In some embodiments, the scatterplot display 250 may enable selection ofa data item, such as data item 252, and display additional informationregarding the data item 252. In further embodiments, the scatterplotdisplay 250, the legend 202, or the category table 254 may enableselection of a category, and display additional information regardingthe category. The category table 254 may display information regardingthe categories, which may be determined dynamically or based onspecified criteria. For example, a category may be specified for dataitems having a risk premium above 1.0 and a risk/loss ratio below 1.0.As a further example, a category may be determined based on the numberof data items analyzed and their relative distance from each other onthe scatterplot, such that a grouping of data items may be determinedbased on the gaps between data items on the scatterplot.

Turning now to FIG. 2D, an example user interface 260 for displayingvarious properties of probability analyses for data items will bedescribed. The user interface 260 may be generated, for example, by thecategorized risk presentation server 108 of FIG. 1. The user interface240 may include a legend 202 and risk model selector 204, which performsimilar functions to those described with reference to FIG. 2A.

The user interface 260 may further include a bar graph display 270,which may display probabilities or other metrics relating to data itemcategories. For example, the bar graph data point 272 may correspond toa risk premium associated with data items in the first category. Theuser interface 260 may include a metric selector 274, which may enablethe user to select the metric to be displayed in the bar graph display270. Alternatively, in some embodiments, selection of a row or column inthe category table 254 of FIG. 2C may pre-populate the user interface260 with a chart of the selected data. As described above, data itemcategories may relate to particular characteristics or attributes ofdata items, such as geographic regions that contain sets of data items,or data items that have been categorized by a risk model. The userinterface 260 may further include other user interface elements, such asa table that displays information relating to individual data items.

FIG. 3 is an example block diagram that depicts generation andpresentation of an aggregate risk analysis. At (1), a client computingdevice requests a risk analysis from the categorized risk presentationserver. In various embodiments, the request may include a category orcategories to be analyzed, a data item or items to be analyzed, a riskmodel or models to be utilized in the analysis, filtering criteria forincluding or excluding data items from the analysis, or other criteria.Although depicted in FIG. 3 as external to the client computing device130, in some embodiments the categorized risk presentation server 108may be implemented as an application or other component that resideswithin the client computing device 130.

At (2), the categorized risk presentation server 108 requests that thecategorized risk modeling server 106 provide risk analysis data. As withthe categorized risk presentation server 108, in some embodiments thecategorized risk modeling server 106 may reside within the clientcomputing device 130, and as described above all or some functions ofthe categorized risk presentation server 108 and the categorized riskmodeling server 106 may be combined or separated in various ways.

At (3), the categorized risk modeling server 106 requests a risk modelfrom the risk model data store 104. Illustratively, the categorized riskmodeling server 106 may determine a risk model based on the request(which, as described above, may include or specify a risk model to use),or may use a predetermined or default risk model. For example, thecategorized risk modeling server 106 may determine a risk model to usebased on characteristics or attributes of data items specified in therequest. In some embodiments, the categorized risk modeling server 106may obtain user preferences that specify a risk model to be used. At(4), the risk model data store 104 provides the requested risk model.

At (5), the categorized risk modeling server 106 requests data itemsthat are associated with one or more categories. In some embodiments, asdescribed above, the categorized risk modeling server 106 determines thecategories based on the request. For example, the request may be for arisk analysis of data items associated with South America. Thecategorized risk modeling server 106 may thus determine the geographicregions corresponding to the countries of South America as thecategories to analyze, and may request data items associated with thesegeographic regions. In some embodiments, the request may specify a levelof granularity, such as a city level, county level, state level, and thelike, and the categorized risk modeling server 106 may determinegeographic regions and corresponding data items based on the specifiedlevel of granularity. At (6), the item data store 102 provides therequested data items.

At (7), the categorized risk modeling server 106 applies the risk modelto the data items and regions to generate aggregated risk analysis datafor the regions. As described above, a risk model may specify variousattributes of data items and regions to include in the model. In someembodiments, the categorized risk modeling server 106 may applyfiltering criteria to include or exclude various data items from themodel. At (8), the categorized risk modeling server 106 provides therisk analysis data to the categorized risk presentation server 108, andat (9) the categorized risk presentation server 108 presents the riskanalysis data to the client computing device 130. In variousembodiments, the categorized risk presentation server 108 generates auser interface, instructions for generating a user interface, or riskanalysis data that may be presented via a user interface to the clientcomputing device 130.

In various embodiments, the interactions depicted in FIG. 3 may becombined, separated, or carried out in different orders than thosedepicted in the example illustration. For example, the interactions at(5) and (6) may precede the interactions at (3) and (4). As a furtherexample, the interactions at (2)-(8) may be carried out independently ofthe interaction at (1), and the resulting risk analysis data may bestored or cached. In some embodiments, the categorized risk presentationserver 108 may interact directly with the risk model data store 104and/or the item data store 102 in order to obtain and presentinformation regarding available risk models and available data items. Infurther embodiments, the client computing device 130 may interactdirectly with the risk model data store 104 and/or the item data store102, obtain data items to be analyzed and/or risk models to be used, andinclude or specify them in the request.

Turning now to FIG. 4, an example routine 400 for analyzing categorizeditems will be described. The routine 400 may be carried out, forexample, by the categorized risk modeling server 106 of FIG. 1. At block402, categorized data items may be obtained. In some embodiments, asdescribed above, filtering criteria may be applied to obtain a subset ofavailable data items. For example, filtering criteria may be applied toobtain only data items that are within North America, that correspond toadult men between the ages of 20 and 45, or other criteria that may beapplied to various attributes of the data item. In further embodiments,categories may be determined for data items based on various data itemattributes.

At block 404, a risk model may be obtained. Illustratively, as describedabove, the risk model may include rules or instructions for determiningdata item risks. For example, the risk model may specify that buildingswithin a particular geographic area are at a base 5% risk of loss due towildfire, that the risk is increased by 2% if the building is made ofwood, decreased by 1% if the building is made of brick, and decreased by1% if certain safety measures have been implemented with regard to thebuilding or the surrounding property. As a further example, the riskmodel may specify that men over the age of 50 are at a base 3% risk oflung cancer, women over the age of 45 are at a base 2% risk of lungcancer, and that the risk is multiplied by 1.5× if the person smokes.One skilled in the art will appreciate that the risk model may be anyset of rules or instructions that may be applied to a data item and usedto model and analyze risks.

At block 406, a data item that has not yet been analyzed may beselected, and at block 408 the data item may be analyzed by applying therisk model. In various embodiments, the risk model may determine risksbased on attributes of the data item, rules or criteria relating to anitem category, relationships between the data item and other data items,or other factors. The risk model may further modify the data item toinclude additional attributes, such as probabilities or risks, or torecalculate risk premiums or other attributes that are based on aparticular risk.

At block 410, a determination may be made as to whether all data itemshave been analyzed. If not, the routine 400 branches to block 406, wherea new data item that has not yet been analyzed may be selected, and theroutine 400 then iterates until all data items have been analyzed. Whenthe determination at block 410 is that all data items have beenanalyzed, the routine 400 branches to block 412, where the categorizeditem analysis may be output. In some embodiments, as described above,the categorized item analysis may be output to the categorized riskpresentation server 108 of FIG. 1, which may generate a user interfacethat displays the item analysis. In other embodiments, the item analysismay be output directly to the client computing device 130 of FIG. 1, orto another device.

One skilled in the art will appreciate that variations on the routine400 are within the scope of the present disclosure. For example, blocks402 and 404 may be carried out in either order, or block 410 may becarried out before block 406. In some embodiments, multiple risk modelsmay be obtained and applied to an item sequentially or in parallel. Infurther embodiments, the analysis generated by applying a first riskmodel may be used by a second risk model in order to analyze morecomplex risks.

FIG. 5 depicts an example routine 500 for aggregating and analyzingrisks that correspond to an item category. The routine 500 may becarried out, for example, by the categorized risk modeling server 106 ofFIG. 1. At block 502, data items may be obtained. In some embodiments,as described above, filtering criteria may be applied to obtain a subsetof available data items. At block 504, a risk model may be obtained,which as described above may include rules or instructions fordetermining data item risks.

At block 506, item categories may be obtained. In some embodiments, itemcategories may be obtained based upon the items that were obtained, suchthat each item is contained in at least one of the categories. In otherembodiments, item categories may be predetermined or specified as partof a request. In further embodiments, a level of granularity may beobtained, and the categories may be obtained by dividing a largercategory into categories. For example, the item categories may begeographic regions, and a level of granularity may specify city blocksor counties. A larger category of data items, such as a city or stateregion, may thus be divided accordingly into categories having therequested level of granularity.

At block 508, an item category that has not yet been analyzed may beselected, and block 510 a subset of the data items may be identified.Illustratively, the subset may include data items associated the itemcategory. For example, the subset may include data items with locationsor areas within an identified geographic region, and/or with areas thatoverlap the geographic region, such as a sea lane or flight path.

At block 512, an aggregate risk may be determined for the item category.Illustratively, the aggregate risk may be determined by using a routine,such as the routine 400 depicted in FIG. 4, to analyze the risks for theindividual items in the subset, and then summing, averaging, sampling,or otherwise processing the item risks to generate an aggregate risk forthe category.

At block 514, a determination may be made as to whether all itemcategories have been analyzed. If not, the routine 500 branches to block508, where another item category may be selected, and the routine 500may then iterate until all item categories have been analyzed. If thedetermination at block 514 is that all item categories have beenanalyzed, then the routine 500 branches to block 516, where the itemcategory analysis may be output. In some embodiments, as describedabove, the item category analysis may be output to the categorized riskpresentation server 108 of FIG. 1, which may generate a user interfacethat displays the item category analysis. In other embodiments, the itemcategory analysis may be output directly to the client computing device130 of FIG. 1, or to another device.

One skilled in the art will appreciate that variations on the routine500 are within the scope of the present disclosure. For example, blocks,502, 504, and 506 may be carried out in any order, or may be carried outseparately prior to the start of routine 500. As a further example,block 514 may be carried out before block 508. In some embodiments,multiple risk models may be obtained and applied to an item categorysequentially or in parallel. In further embodiments, the analysisgenerated by applying a first risk model may be used by a second riskmodel in order to analyze more complex risks.

ADDITIONAL IMPLEMENTATION DETAILS AND EMBODIMENTS

Various embodiments of the present disclosure may be a system, a method,and/or a computer program product at any possible technical detail levelof integration. The computer program product may include a computerreadable storage medium (or mediums) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

For example, the functionality described herein may be performed assoftware instructions are executed by, and/or in response to softwareinstructions being executed by, one or more hardware processors and/orany other suitable computing devices. The software instructions and/orother executable code may be read from a computer readable storagemedium (or mediums).

The computer readable storage medium can be a tangible device that canretain and store data and/or instructions for use by an instructionexecution device. The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device (includingany volatile and/or non-volatile electronic storage devices), a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a solid state drive, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions (as also referred to herein as,for example, “code,” “instructions,” “module,” “application,” “softwareapplication,” and/or the like) for carrying out operations of thepresent disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. Computer readable program instructions may be callable fromother instructions or from itself, and/or may be invoked in response todetected events or interrupts. Computer readable program instructionsconfigured for execution on computing devices may be provided on acomputer readable storage medium, and/or as a digital download (and maybe originally stored in a compressed or installable format that requiresinstallation, decompression or decryption prior to execution) that maythen be stored on a computer readable storage medium. Such computerreadable program instructions may be stored, partially or fully, on amemory device (e.g., a computer readable storage medium) of theexecuting computing device, for execution by the computing device. Thecomputer readable program instructions may execute entirely on a user'scomputer (e.g., the executing computing device), partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart(s) and/or block diagram(s)block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks. For example, the instructions may initially be carried on amagnetic disk or solid state drive of a remote computer. The remotecomputer may load the instructions and/or modules into its dynamicmemory and send the instructions over a telephone, cable, or opticalline using a modem. A modem local to a server computing system mayreceive the data on the telephone/cable/optical line and use a converterdevice including the appropriate circuitry to place the data on a bus.The bus may carry the data to a memory, from which a processor mayretrieve and execute the instructions. The instructions received by thememory may optionally be stored on a storage device (e.g., a solid statedrive) either before or after execution by the computer processor.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. In addition, certain blocks may be omitted insome implementations. The methods and processes described herein arealso not limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate.

It will also be noted that each block of the block diagrams and/orflowchart illustration, and combinations of blocks in the block diagramsand/or flowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions. For example, any of the processes, methods, algorithms,elements, blocks, applications, or other functionality (or portions offunctionality) described in the preceding sections may be embodied in,and/or fully or partially automated via, electronic hardware suchapplication-specific processors (e.g., application-specific integratedcircuits (ASICs)), programmable processors (e.g., field programmablegate arrays (FPGAs)), application-specific circuitry, and/or the like(any of which may also combine custom hard-wired logic, logic circuits,ASICs, FPGAs, etc. with custom programming/execution of softwareinstructions to accomplish the techniques).

Any of the above-mentioned processors, and/or devices incorporating anyof the above-mentioned processors, may be referred to herein as, forexample, “computers,” “computer devices,” “computing devices,” “hardwarecomputing devices,” “hardware processors,” “processing units,” and/orthe like. Computing devices of the above-embodiments may generally (butnot necessarily) be controlled and/or coordinated by operating systemsoftware, such as Mac OS, iOS, Android, Chrome OS, Windows OS (e.g.,Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, WindowsServer, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS,VxWorks, or other suitable operating systems. In other embodiments, thecomputing devices may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, I/O services, and provide a user interface functionality,such as a graphical user interface (“GUI”), among other things.

For example, FIG. 6 is a block diagram that illustrates a computersystem 800 upon which various embodiments may be implemented. Computersystem 800 includes a bus 802 or other communication mechanism forcommunicating information, and a hardware processor, or multipleprocessors, 804 coupled with bus 802 for processing information.Hardware processor(s) 804 may be, for example, one or more generalpurpose microprocessors.

Computer system 800 also includes a main memory 806, such as a randomaccess memory (RAM), cache and/or other dynamic storage devices, coupledto bus 802 for storing information and instructions to be executed byprocessor 804. Main memory 806 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 804. Such instructions, whenstored in storage media accessible to processor 804, render computersystem 800 into a special-purpose machine that is customized to performthe operations specified in the instructions.

Computer system 800 further includes a read only memory (ROM) 808 orother static storage device coupled to bus 802 for storing staticinformation and instructions for processor 804. A storage device 810,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 802 for storing information andinstructions.

Computer system 800 may be coupled via bus 802 to a display 812, such asa cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 814,including alphanumeric and other keys, is coupled to bus 802 forcommunicating information and command selections to processor 804.Another type of user input device is cursor control 816, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 804 and for controllingcursor movement on display 812. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

Computing system 800 may include a user interface module to implement aGUI that may be stored in a mass storage device as computer executableprogram instructions that are executed by the computing device(s).Computer system 800 may further, as described below, implement thetechniques described herein using customized hard-wired logic, one ormore ASICs or FPGAs, firmware and/or program logic which in combinationwith the computer system causes or programs computer system 800 to be aspecial-purpose machine. According to one embodiment, the techniquesherein are performed by computer system 800 in response to processor(s)804 executing one or more sequences of one or more computer readableprogram instructions contained in main memory 806. Such instructions maybe read into main memory 806 from another storage medium, such asstorage device 810. Execution of the sequences of instructions containedin main memory 806 causes processor(s) 804 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions.

Various forms of computer readable storage media may be involved incarrying one or more sequences of one or more computer readable programinstructions to processor 804 for execution. For example, theinstructions may initially be carried on a magnetic disk or solid statedrive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 800 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 802. Bus 802 carries the data tomain memory 806, from which processor 804 retrieves and executes theinstructions. The instructions received by main memory 806 mayoptionally be stored on storage device 810 either before or afterexecution by processor 804.

Computer system 800 also includes a communication interface 818 coupledto bus 802. Communication interface 818 provides a two-way datacommunication coupling to a network link 820 that is connected to alocal network 822. For example, communication interface 818 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 818 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicated with a WAN). Wireless links may also be implemented. In anysuch implementation, communication interface 818 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 820 typically provides data communication through one ormore networks to other data devices. For example, network link 820 mayprovide a connection through local network 822 to a host computer 824 orto data equipment operated by an Internet Service Provider (ISP) 826.ISP 826 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 828. Local network 822 and Internet 828 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 820and through communication interface 818, which carry the digital data toand from computer system 800, are example forms of transmission media.

Computer system 800 can send messages and receive data, includingprogram code, through the network(s), network link 820 and communicationinterface 818. In the Internet example, a server 830 might transmit arequested code for an application program through Internet 828, ISP 826,local network 822 and communication interface 818.

The received code may be executed by processor 804 as it is received,and/or stored in storage device 810, or other non-volatile storage forlater execution.

As described above, in various embodiments certain functionality may beaccessible by a user through a web-based viewer (such as a web browser),or other suitable software program). In such implementations, the userinterface may be generated by a server computing system and transmittedto a web browser of the user (e.g., running on the user's computingsystem). Alternatively, data (e.g., user interface data) necessary forgenerating the user interface may be provided by the server computingsystem to the browser, where the user interface may be generated (e.g.,the user interface data may be executed by a browser accessing a webservice and may be configured to render the user interfaces based on theuser interface data). The user may then interact with the user interfacethrough the web-browser. User interfaces of certain implementations maybe accessible through one or more dedicated software applications. Incertain embodiments, one or more of the computing devices and/or systemsof the disclosure may include mobile computing devices, and userinterfaces may be accessible through such mobile computing devices (forexample, smartphones and/or tablets).

Many variations and modifications may be made to the above-describedembodiments, the elements of which are to be understood as being amongother acceptable examples. All such modifications and variations areintended to be included herein within the scope of this disclosure. Theforegoing description details certain embodiments. It will beappreciated, however, that no matter how detailed the foregoing appearsin text, the systems and methods can be practiced in many ways. As isalso stated above, it should be noted that the use of particularterminology when describing certain features or aspects of the systemsand methods should not be taken to imply that the terminology is beingre-defined herein to be restricted to including any specificcharacteristics of the features or aspects of the systems and methodswith which that terminology is associated.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements, and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

The term “substantially” when used in conjunction with the term“real-time” forms a phrase that will be readily understood by a personof ordinary skill in the art. For example, it is readily understood thatsuch language will include speeds in which no or little delay or waitingis discernible, or where such delay is sufficiently short so as not tobe disruptive, irritating, or otherwise vexing to a user.

Conjunctive language such as the phrase “at least one of X, Y, and Z,”or “at least one of X, Y, or Z,” unless specifically stated otherwise,is to be understood with the context as used in general to convey thatan item, term, etc. may be either X, Y, or Z, or a combination thereof.For example, the term “or” is used in its inclusive sense (and not inits exclusive sense) so that when used, for example, to connect a listof elements, the term “or” means one, some, or all of the elements inthe list. Thus, such conjunctive language is not generally intended toimply that certain embodiments require at least one of X, at least oneof Y, and at least one of Z to each be present.

The term “a” as used herein should be given an inclusive rather thanexclusive interpretation. For example, unless specifically noted, theterm “a” should not be understood to mean “exactly one” or “one and onlyone”; instead, the term “a” means “one or more” or “at least one,”whether used in the claims or elsewhere in the specification andregardless of uses of quantifiers such as “at least one,” “one or more,”or “a plurality” elsewhere in the claims or specification.

The term “comprising” as used herein should be given an inclusive ratherthan exclusive interpretation. For example, a general purpose computercomprising one or more processors should not be interpreted as excludingother computer components, and may possibly include such components asmemory, input/output devices, and/or network interfaces, among others.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it may beunderstood that various omissions, substitutions, and changes in theform and details of the devices or processes illustrated may be madewithout departing from the spirit of the disclosure. As may berecognized, certain embodiments of the inventions described herein maybe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features may be used or practicedseparately from others. The scope of certain inventions disclosed hereinis indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A system comprising: a data store configured tostore data items and probabilities; and a processor in communicationwith the data store, the processor configured to execute specificcomputer-executable instructions to at least: receive informationregarding a first data item; determine a geographic location of thefirst data item; determine, based at least in part on a risk model, anevent probability associated with a geographic region, the eventprobability indicating a probability that an event affecting thegeographic region will occur, the geographic region including thegeographic location of the first data item; obtain, from the data store,a plurality of data items, wherein each of the plurality of data itemsis associated with a respective geographic location in the geographicregion; determine, based at least in part on the risk model, the eventprobability, and one or more attributes of the first data item, aprobability that the event will change a first attribute of the firstdata item, and a predicted attribute change to the first attribute ofthe first data item; for individual data items of the plurality of dataitems, determine, based at least in part on the risk model, the eventprobability, and one or more attributes of the data item, a probabilitythat the event will change the first attribute of the data item, and apredicted change to the first attribute of the data item; determine aprobability associated with the geographic region based at least in parton the event probability, the probabilities that the event will changethe first attributes, and the predicted changes to the first attributes;determine a probability category of the first data item based at leastin part on: the probability associated with the geographic region, theprobability that the event will change the first attribute of the firstdata item, and the probabilities that the event will change the firstattribute of individual data items of the plurality of data items;generate for display a user interface, the user interface including atleast: a geographic map identifying the geographic region, thegeographic location of the first data item, and the geographic locationsof the plurality of items, wherein a shading of an icon displayed at thegeographic location of the first data item indicates the probabilitycategory of the first data item, and wherein a size of the iconindicates the predicted change to the first attribute of the first dataitem; and cause display of the user interface.
 2. The system of claim 1,wherein the data store is further configured to store geographicregions, and wherein the processor is configured to obtain thegeographic region from the data store.
 3. The system of claim 1, whereinthe processor is configured to determine the probability associated withthe geographic region based at least in part on one or more previousevents associated with the geographic region.
 4. The system of claim 3,wherein the processor configured to determine the probability associatedwith the geographic region is configured to: determine the probabilityassociated with the geographic region based at least in part on apredicted change to a second attribute of individual data items withinthe plurality of data items.
 5. The system of claim 1, wherein thegeographic map includes at least the geographic region.
 6. The system ofclaim 5, wherein the map display further includes an area of interest.7. The system of claim 6, wherein the area of interest comprises atleast one of a storm track, weather track, flood plain, drought zone,earthquake zone, tsunami zone, avalanche zone, tornado zone, volcanozone, or wildfire zone.
 8. The system of claim 6, wherein the area ofinterest comprises a predicted weather track.
 9. The system of claim 6,wherein the area of interest comprises a geographic route.
 10. Thesystem of claim 1, wherein the user interface further includes at leastone of a scatterplot display or a bar chart display.
 11. The system ofclaim 1, wherein the data store is further configured to store riskmodels, and wherein the processor is further configured to: obtain, fromthe data store, the risk model.
 12. The system of claim 11, wherein theprocessor is further configured to determine the probability associatedwith the geographic region based at least in part on the risk model. 13.The system of claim 11, wherein the processor is further configured toreceive, via the user interface, an indication of a selection of therisk model.
 14. The system of claim 1, wherein the processor is furtherconfigured to: receive, via the user interface, an indication of an areaof interest; determine a subset of the plurality of data items, whereineach data item of the plurality of data items is associated with ageographic location within the area of interest; generate a second userinterface, the second user interface including at least the subset ofthe plurality of data items; and cause display of the second userinterface.
 15. The system of claim 1, wherein the probability categoryof the first data item comprises a high risk category.
 16. The system ofclaim 1, wherein the probability category of the first data itemcomprises a category of data items having a high risk-to-loss ratio. 17.A system comprising: a data store configured to store data items,respective geographic locations associated with the data items, andrespective probabilities associated with the data items; and a processorin communication with the data store, the processor configured toexecute specific computer-executable instructions to at least: receivean indication of geographic grouping criteria; determine, based on thegeographic grouping criteria, a plurality of geographic regions; analyzethe data items and associated geographic locations to determine aplurality of groups of data items associated with respective geographicregions; for each group in the plurality of groups of data items,analyze the data items of the group to determine, based at least in parton a risk model and one or more attributes of individual data items ofthe group, an aggregate probability associated with the group of dataitems, the aggregate probability indicating a level of exposure to arisk associated with at least a portion of the respective geographicregion; and generate user interface data useable for rendering a userinterface, the user interface including graphical representations of thegeographic regions, wherein the graphical representations includevisualizations indicative of the aggregate probabilities associated withthe groups associated with the geographic regions and visualizations ofindividual data items from at least one group of data items, wherein afirst attribute of a visualization of an individual data item indicatesa probability category of the individual data item, and wherein a secondattribute of the visualization of the individual data item indicates apredicted change to the one or more attributes of the individual dataitem.
 18. The system of claim 17, wherein the processor is furtherconfigured to receive an indication of filtering criteria.
 19. Thesystem of claim 18, wherein the processor is further configured todetermine the plurality of groups of data items based at least in parton the filtering criteria.
 20. The system of claim 17, wherein thegeographic grouping criteria specify a granularity of the plurality ofgeographic regions.